Development and Validation of a Prediction Model Using Sella Magnetic Resonance Imaging–Based Radiomics and Clinical Parameters for the Diagnosis of Growth Hormone Deficiency and Idiopathic Short Stature: Cross-Sectional, Multicenter Study
Our model combining both radiomics and clinical parameters can accurately predict GHD from ISS, which was also proven in the external validation. These findings highlight the potential of machine learning-based models using radiomics and clinical parameters for diagnosing GHD and ISS.
Authors
- Hyun Wook Chae (ORCID: https://orcid.org/0000-0001-5016-8539)
- Hyun Joo Shin (ORCID: https://orcid.org/0000-0002-7462-2609)
- Chae Jung Park (ORCID: https://orcid.org/0000-0002-5567-8658)
- Ho-Seong Kim (ORCID: https://orcid.org/0000-0003-1135-099X)
- Beomseok Sohn (ORCID: https://orcid.org/0000-0002-6765-8056)
- Taehoon Ko (ORCID: https://orcid.org/0000-0002-4045-0036)
- Jihoon Na (ORCID: https://orcid.org/0000-0002-3051-2010)
- Kyungchul Song (ORCID: https://orcid.org/0000-0002-8497-5934)
- Jun Suk Oh (ORCID: https://orcid.org/0000-0002-5765-2347)
- Jeong‐Ho Kim (ORCID: https://orcid.org/0000-0003-2479-0548)
Institutions
- Catholic Medical Center (US)
- Konyang University (KR)
- Yonsei University (KR)
- Severance Hospital (KR)
- Samsung Medical Center (KR)
- Sungkyunkwan University (KR)
- Catholic University of Korea (KR)
Publication Details
- Journal
- Journal of Medical Internet Research
- Published
- 2024-10-02
- DOI
- https://doi.org/10.2196/54641
- Citations
- 4
- Primary Topic
- Radiomics and Machine Learning in Medical Imaging
- Type
- article
- Field-Weighted Citation Impact
- 1.62